def test_thresholded_brownian_probability(self):
"""Brownian prob should return dict of state probabilities"""
#x = thresholded_brownian_probability(2.2755, 1001**0.5, 0.03, min_val = 0.0,increment = 1.00,trait_prob_cutoff = 1e-4)
#lines = ["\t".join(map(str,[k,x[k]]))+"\n" for k in sorted(x.keys())]
#for line in lines:
# print line
#print "Total prob:", sum(x.values())
start_state = 3.0
var = 30.00
d = 0.03
min_val = 0.0
increment = 1.0
trait_prob_cutoff = 1e-200
obs = thresholded_brownian_probability(start_state,d,var,min_val,\
increment,trait_prob_cutoff)
#TODO: Need to calculate exact values for this minimal case
#with the Larson & Farber Z tables, by hand.
#For now test for sanity
#Test that no probabilities below threshold are included
self.assertTrue(min(obs.values()) > trait_prob_cutoff)
#Test that start values +1 or -1 are equal
self.assertEqual(obs[2.0],obs[4.0])
#Test that the start state is the highest prob value
self.assertEqual(max(obs.values()),obs[start_state])
def test_thresholded_brownian_probability(self):
"""Brownian prob should return dict of state probabilities"""
#x = thresholded_brownian_probability(2.2755, 1001**0.5, 0.03, min_val = 0.0,increment = 1.00,trait_prob_cutoff = 1e-4)
#lines = ["\t".join(map(str,[k,x[k]]))+"\n" for k in sorted(x.keys())]
#for line in lines:
# print line
#print "Total prob:", sum(x.values())
start_state = 3.0
var = 30.00
d = 0.03
min_val = 0.0
increment = 1.0
trait_prob_cutoff = 1e-200
obs = thresholded_brownian_probability(start_state,d,var,min_val,\
increment,trait_prob_cutoff)
#TODO: Need to calculate exact values for this minimal case
#with the Larson & Farber Z tables, by hand.
#For now test for sanity
#Test that no probabilities below threshold are included
self.assertTrue(min(obs.values()) > trait_prob_cutoff)
#Test that start values +1 or -1 are equal
self.assertEqual(obs[2.0],obs[4.0])
#Test that the start state is the highest prob value
self.assertEqual(max(obs.values()),obs[start_state])